Tag: Hyper-Personalistion

Over the last 30 years we have witnessed the very nature of marketing change. We have seen the focus shift from creative advertising, through digital marketing and mobile apps to the current focus on data-driven engagement and data-centric marketing. The future, however, will be machine learning, artificial intelligence and predictive marketing.

According to New York Times journalists, John Markoff and Steve Lohr, many of the tech industry’s biggest companies like Amazon, Google, IBM and Microsoft, are jockeying to become the go-to company for A.I. by 2020. The market for machine learning applications will reach $40 billion market research firm IDC estimates. And 60 percent of those applications will run on the platform software of just four companies — Amazon, Google, IBM and Microsoft.

In the very near future, intelligent software applications will become commonplace and machine learning will touch every industry. Today, only about one percent of all software apps have A.I. features, IDC estimates. By 2018, IDC predicts that at least 50 percent of developers will include A.I. features in what they create.

We are already seeing an epochal shift in how businesses operate and compete with the development of a new generation of companies using this type of technology and engage with customers one-to- one. These companies use data to personalise and predict what its customers are going to do next and then engage them with intelligent marketing programs.

The most disruptive companies in the world, for instance Uber, Amazon and Netflix, have one thing in common. And that is that they drive business growth and rapid global roll out using data-driven, personalised, predictive and responsive engagement. Those companies that can unlock the power and value of data are seeing higher engagement and viewership and higher revenues from customers. For example, the majority of Amazon’s and Netflix’s sales are driven off predictive marketing engines using machine learning technology.

If you’re still not convinced, maybe this example will help. In recent years, the supermarket giant Tesco dropped the amount of offers it sent out by nearly two-thirds but massively increased its revenue from conversion and ROI by using this type of technology. Tesco achieved a 675 percent growth in its bottom line, as a result of its data-driven loyalty programme. By analysing customer data, Tesco found that 80 percent of the discounts and offers utilised by customers came from 20 percent of the offers generated. This prompted a reduction of offers from 750 to 300 a year,amounting to approximately $600 million savings in promotion expenses while increasing market share.

Machine learning creates enormous efficiencies because you no longer have teams of campaign managers generating and managing hundreds of offers – many of which are actually delivering negative ROI. So instead of sending out 100 offers, you can send out 30 offers but each one of those 30 offers will have a much higher conversion. And not only is that a much better use of a business’s time and energy but it will also surprise and delight your customers when they start to receive offers that actually matter to them.

Wouldn’t you like to know what your customers are going to buy next and contact them with a product offer exactly when they are thinking about it? Then visit Quantiful’s website and get in touch.

Trust Quantiful for state of the art precision marketing, machine learning and hyper-personalistion services; designed to offer results.

Machine learning is a form of artificial intelligence that empowers computers with the ability to learn without being explicitly programmed. Machine learning, in other words, can be defined as a subfield of computer science that focuses on the development of computer programs which can actually teach themselves to change, grow and modify when exposed to new sets of data.

Machine learning is related to data mining. Both of these systems carefully search through massive pools of data to find strategic patterns. However, the difference is machine learning not only detect patterns but it then modifies and adjusts program actions. As such, it self-teaches to evolve and mature when exposed to data.

Facebook’s News Feeds, for example, leverage machine learning to personalise each of its member’s feeds. So if you stop scrolling in order to read, comment or like a specific friend’s posts, Facebook News Feed will show you more of that particular friend’s activity in your feed.

Facebook can do this because the software behind its News Feeds simply uses statistical analysis and predictive analytics to detect patterns in your browsing data and then utilises the findings of these patterns to populate your News Feed. If you stop reading or liking that friend’s posts, this new behaviour or pattern will be included in the data set and your News Feed will be updated accordingly.

Another example is Jaguar Land Rover New cars built by Jaguar Land Rover have around 60 on board computers that produce a massive 1.5 GB of data daily across over20,000 specialised and defined metrics. Engineers at the company leverage machine learning to analyse the data and understand how customers actually behave with the vehicle. Machine learning helps engineers design better vehicles which are tailored according to their customers’ personalised requirements. Also, and perhaps more importantly, by utilising and harnessing the true power of the data,designers can predict potential safety issues with their cars.

Why Is Machine Learning So Important Today?

Machine learning has become popular due to a range of factors such as growing volumes and varieties of data, affordable computational processing and more secured data storage. Because of this, it is now possible to securely and automatically produce models that can analyse bigger and more complex data and deliver faster, precise and accurate results.

To learn more about how machine learning, data science and hyper personalization can help your business, please visit Quantiful‘s website.

Social analytics means the collection and analysis of data, and statistics about how customers interface with an organization online.

Over the last few decades, social analytics have become one of the most crucial forms of informed business intelligence, which is leveraged to gather customer data, predict their behaviours and respond to their actions.

In our everyday life, whenever we browse an online shopping store, use a credit card to buy a product, or share special discounted offers from our favourite mobile brand on our social networks, we are continually throwing out hints of intelligence. These hints are goldmine of information for brands who want to learn about us, our behaviours and patterns.

With every single click that we make online, specific data about our online activities are being collected and it is now very rare to find any website that does not collect user data in one way or the other. Some websites use a specific social analytics and customer activities monitoring tool, while others use various tools to do the job.

In its most basic form, website owners use a generic and popular social analytics tool, such as Google Analytics, to capture, analyse, decipher and use data. Some of the data gathered from these tools include unique website visits, most viewed pages, search terms used to find the website and the physical location of the visitors. There are many other advanced set of data which can be gathered using basic social analytics tools. The core purpose in gathering the information is to understand how to make the website a better experience for your customers.

Other than Google analytics, there are many other social analytics tools which offer better reporting features. The data gathered from these tools can help a company better understand its audience, their behaviours and activities. The data helps organisations measure their return on investment (ROI) on their social media strategies, and to then how to plan for the future use of social media to generate profit.